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Improving the Reliability of Autonomous Vehicles in a Branded Service System Using Big Data

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dc.contributor.author Makarova I.
dc.contributor.author Buyvol P.
dc.contributor.author Gabsalikhova L.
dc.contributor.author Pashkevich A.
dc.contributor.author Tsybunov E.
dc.contributor.author Boyko A.
dc.date.accessioned 2021-02-25T06:55:23Z
dc.date.available 2021-02-25T06:55:23Z
dc.date.issued 2020
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/161538
dc.description.abstract © 2020 IEEE. In the 4th industrial revolution era, the transport's role acquires a special role in organizing the functioning of large systems (production, logistics, service, etc.). Technical and technological changes (for example, the appearance of autonomous vehicles leads to a shift in emphasis and roles: the influence of the human factor is reduced and issues of increasing reliability and safety are of particular importance. Monitoring vehicle technical condition and its diagnostics are important during the operation phase, because this eliminates the possible failures causes and predicted the vehicle life cycle, as well as the define pre-failure conditions. Besides, the service organization problems are also relevant because the transport system security depends more and more on the vehicles technical condition, communications, and infrastructure facilities. The article proposes a possible solution to manage the service system by creating common information space that based on relevant information and the new analysis methods uses, can increase the transport system's efficiency and reliability as a whole. The structure of the decision support system is proposed. It includes modules for data collection, storage, data mining, decision-making and a user interface. To process the accumulated data, the use of OLAP technologies is proposed.
dc.subject autonomous vehicles
dc.subject big data
dc.subject branded service system
dc.subject reliability
dc.title Improving the Reliability of Autonomous Vehicles in a Branded Service System Using Big Data
dc.type Conference Paper
dc.collection Публикации сотрудников КФУ
dc.source.id SCOPUS-2020-SID85100319017


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  • Публикации сотрудников КФУ Scopus [24551]
    Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.

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